{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cox model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from pymc3 import Gamma, Poisson, Normal, Model, sample, forestplot, NUTS, Metropolis, find_MAP, starting, traceplot\n",
    "import theano.tensor as tt\n",
    "from theano import function as fn\n",
    "from theano import printing\n",
    "import numpy as np\n",
    "import scipy as sp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the original model, implemented in BUGS:\n",
    "\n",
    "```R\n",
    "model\n",
    "{   \n",
    "    # Set up data\n",
    "        for(i in 1:Nsubj) {\n",
    "            for(j in 1:T) {\n",
    "    # risk set = 1 if obs.t >= t\n",
    "                Y[i,j] <- step(obs.t[i] - t[j] + eps)\n",
    "    # counting process jump = 1 if obs.t in [ t[j], t[j+1] )\n",
    "    #                      i.e. if t[j] <= obs.t < t[j+1]\n",
    "                dN[i, j] <- Y[i, j] * step(t[j + 1] - obs.t[i] - eps) * FAIL[i]\n",
    "            }\n",
    "        }\n",
    "\n",
    "    # Model \n",
    "        for(j in 1:T) {\n",
    "            for(i in 1:Nsubj) {\n",
    "                dN[i, j]   ~ dpois(Idt[i, j])              # Likelihood\n",
    "                Idt[i, j] <- Y[i, j] * exp(beta[1]*pscenter[i] + beta[2]*\n",
    "                hhcenter[i] + beta[3]*ncomact[i] + beta[4]*rleader[i] + beta[5]*dleader[i] + beta[6]*inter1[i] + beta[7]*inter2[i]) * dL0[j]    # Intensity\n",
    "            }     \n",
    "            dL0[j] ~ dgamma(mu[j], c)\n",
    "            mu[j] <- dL0.star[j] * c    # prior mean hazard \n",
    "        }\n",
    "\n",
    "\n",
    "    c ~ dgamma(0.0001, 0.00001)\n",
    "    r ~ dgamma(0.001, 0.0001)\n",
    "\n",
    "\n",
    "    for (j in 1 : T) {  dL0.star[j] <- r * (t[j + 1] - t[j])  } \n",
    "    # next line indicates number of covariates and is for the corresponding betas\n",
    "    for(i in 1:7) {beta[i] ~ dnorm(0.0,0.00001)} \n",
    "\n",
    "\n",
    "}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dta = dict(T=73, Nsubj=430, eps=0.0,  t=[1, 21, 85, 128, 129, 148, 178, 204,\n",
    "                                         206, 210, 211, 212, 225, 238, 241,\n",
    "                                         248, 259, 273, 275, 281, 286, 289,\n",
    "                                         301, 302, 303, 304, 313, 317, 323,\n",
    "                                         344, 345, 349, 350, 351, 355, 356,\n",
    "                                         359, 364, 385, 386, 389, 390, 391,\n",
    "                                         392, 394, 395, 396, 397, 398, 399,\n",
    "                                         400, 406, 415, 416, 426, 427, 434,\n",
    "                                         435, 437, 441, 447, 448, 449, 450,\n",
    "                                         451, 453, 455, 456, 458, 459, 460,\n",
    "                                         461, 462, 463],\n",
    "obs_t = [460, 313, 435, 350, 435, 350, 350, 460, 460, 448, 225, 225, 396, 435, 396, 396, 453, 396, 456, 397, 397, 396, 395, 275, 449, 395, 395, 462, 302, 302, 458, 461, 396, 241, 389, 458, 304, 304, 395, 395, 364, 460, 415, 463, 396, 459, 441, 435, 396, 458, 437, 396, 356, 356, 396, 455, 396, 462, 399, 400, 350, 350, 395, 395, 441, 355, 85, 458, 128, 396, 386, 386, 386, 462, 458, 390, 390, 396, 396, 396, 427, 458, 395, 275, 275, 395, 359, 395, 395, 441, 395, 463, 178, 275, 463, 396, 396, 259, 396, 396, 458, 441, 396, 463, 396, 463, 435, 396, 437, 396, 398, 463, 460, 462, 460, 460, 210, 396, 435, 458, 385, 323, 323, 359, 396, 396, 460, 238, 441, 450, 392, 458, 396, 458, 396, 396, 462, 435, 396, 394, 396, 435, 458, 1, 395, 395, 451, 462, 458, 462, 396, 286, 396, 349, 449, 462, 455, 21, 463, 461, 461, 456, 435, 396, 460, 462, 462, 435, 435, 460, 386, 396, 458, 386, 461, 441, 435, 435, 463, 456, 396, 275, 460, 406, 460, 406, 317, 406, 461, 396, 359, 458, 463, 435, 462, 458, 396, 396, 273, 396, 435, 281, 275, 396, 447, 225, 447, 396, 435, 416, 396, 248, 396, 435, 435, 396, 461, 385, 396, 458, 458, 396, 461, 396, 448, 396, 396, 460, 455, 456, 463, 462, 458, 463, 396, 462, 395, 456, 396, 463, 396, 435, 459, 396, 396, 396, 395, 435, 455, 395, 461, 344, 396, 395, 396, 317, 396, 395, 426, 461, 396, 289, 441, 395, 396, 458, 396, 396, 435, 396, 395, 396, 441, 345, 396, 359, 435, 435, 396, 396, 395, 458, 461, 458, 212, 301, 458, 456, 395, 396, 395, 435, 396, 396, 303, 458, 460, 400, 396, 462, 359, 458, 396, 206, 441, 396, 458, 396, 462, 396, 396, 275, 396, 395, 435, 435, 462, 225, 458, 462, 396, 396, 289, 396, 303, 455, 400, 400, 359, 461, 396, 462, 460, 463, 463, 463, 204, 435, 435, 396, 396, 396, 463, 458, 396, 455, 435, 396, 396, 463, 396, 461, 463, 460, 441, 460, 435, 435, 460, 455, 460, 395, 460, 460, 460, 435, 449, 463, 462, 129, 391, 396, 391, 391, 434, 356, 462, 396, 349, 225, 396, 435, 461, 391, 391, 351, 211, 461, 212, 434, 148, 356, 458, 456, 455, 435, 463, 463, 462, 435, 463, 437, 460, 396, 406, 451, 460, 435, 396, 460, 455, 396, 398, 456, 458, 396, 456, 449, 396, 128, 396, 462, 463, 396, 396, 396, 435, 460, 396, 458],\n",
    "FAIL= [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
    "pscenter= [\n",
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    "inter1= [ -.01434325, -.01460965, 0, 0, 0, -.01113493, 0, 0, 0, -.0553269, -.03238896, 0, 0, -.07062459, -.07464545, -.07032613, 0, 0, -.01408955, 0, -.00219072, 0, 0, 0, 0, 0, .07300876, .01394272, 0, 0, 0, 0, 0, 0, .05120398, 0, -.00550709, -.02062663, -.03077685, -.01688493, 0, .01149963, 0, .01149963, .01149963, 0, 0, 0, 0, 0, 0, 0, 0, 0, .01149963, .0034338, .0376236, .00733331, 0, .03832785, .03832785, -.02622275, -.02622275, -.02622275, -.01492678, 0, 0, -.02897806, -.02847666, 0, 0, -.04224754, -.04743705, -.0510477, -.031893, 0, 0, 0, -.01503116, .003101, -.00083466, .02395027, -.07952866, 0, 0, -.06586029, 0, -.0613939, -.081205, -.07540084, -.08488011, -.08488011, 0, -.07492433, -.08907269, -.09451609, 0, -.08980743, 0, -.0771635, 0, 0, -.0771635, -.08204606, 0, -.05263504, 0, -.05109092, -.04696729, 0, -.04696729, 0, -.05303248, -.05348096, 0, 0, .00584956, -.00792241, -.01719816, 0, -.01576016, 0, -.04014061, 0, 0, 0, 0, 0, .0471441, 0, .04233112, 0, .04233112, 0, 0, .0493324, .04512087, .03205975, .02913185, 0, .05324252, 0, 0, 0, 0, .05054695, 0, .14026688, .01734403, .06078221, 0, 0, 0, -.03138622, 0, .01637333, 0, 0, 0, 0, .01897239, .01591935, 0, -.0619156, 0, -.06851645, 0, -.03889525, -.05023452, -.05013452, 0, 0, -.01362136, 0, 0, -.02634164, 0, 0, 0, 0, -.00890537, -.00611669, 0, 0, 0, -.01513384, 0, -.03551984, 0, -.01978032, 0, .06706496, .10551275, 0, .03092981, .06556855, 0, 0, 0, .09362991, 0, 0, 0, 0, 0, 0, .02610553, .03546937, 0, 0, .034415, 0, 0, 0, .07546701, 0, 0, 0, 0, -.02919447, -.01016712, 0, 0, 0, 0, -.04845615, -.05010044, 0, 0, 0, 0, 0, 0, -.07666632, 0, 0, -.07226554, -.08216553, -.0777643, 0, 0, -.04727952, 0, -.06870384, -.05999847, 0, 0, 0, .02772475, .02883079, .03642944, 0, .04148949, 0, 0, 0, .04268012, .03225577, 0, -.05140995, -.05399637, 0, 0, .02432223, 0, .0490674, .0490674, .0490674, 0, 0, 0, 0, 0, 0, 0, 0, .10476315, 0, 0, 0, 0, 0, .07008056, 0, 0, .01667466, 0, .05253941, .04293926, 0, .02692172, 0, 0, .08742411, .04533176, 0, .01831875, 0, .09834951, .09952456, 0, .02945534, .038731, 0, .04435538, 0, -.02357505, 0, 0, -.02357505, .09324722, 0, 0, 0, -.03490683, 0, -.05054474, 0, -.0474724, -.04905931, 0, .02879751, 0, 0, 0, 0, 0, 0, 0, .04439012, 0, .02989959, .02989959, .05468828, .04463226, 0, 0, 0, 0, 0, .01231324, -.01399783, .04595331, .00145386, 0, .06459354, -.0007196, 0, -.07614055, -.08435525, 0, -.10299519, 0, 0, 0, -.00210284, -.00797183, 0, 0, 0, 0, -.03545086, 0, 0, 0, 0, -.061286, -.07666647, 0, -.05902354, -.07652324, -.07645561, 0, 0, 0, -.03292062, 0, 0, 0, 0, -.075417, 0, -.07922532, 0, -.08583414, -.07450142, -.08066016, 0, 0, -.06249051, 0, 0, 0, 0, -.0618688, 0, -.06524737, -.04419825, -.04489509, 0, 0, 0, -.04520512, -.04187583, 0, 0, -.03753508, 0, 0, 0, 0, 0, 0, 0, 0, .06862645, 0, 0, -.00120631, .01947345, 0, 0, .03561932, 0, .03158225, .03608047, 0, 0, 0, -.02899643],\n",
    "\n",
    "inter2= [-.78348798, -.63418788, 0, 0, 0, .11481193, 0, 0, 0, -.88128799, -1.109488, 0, 0, -.30888793, .29651192, -.36688802, 0, 0, -.59088796, 0, .50561196, 0, 0, 0, 0, 0, 2.1662121, .08891205, 0, 0, 0, 0, 0, 0, -.23918791, 0, -.9575879, -.07728811, .29641202, 1.2273121, 0, 1.5764117, 0, .72131211, 1.279212, 0, 0, 0, 0, 0, 0, 0, 0, 0, .36481193, 1.5480118, -.03078791, 1.389112, 0, .70901209, -.16668792, 1.435812, .47001198, 2.0838118, 1.1673121, 0, 0, 1.4470119, .23301201, 0, 0, -.61948794, -.41388795, .263212, .66171199, 0, 0, 0, 1.6920118, 1.334012, 1.2101121, .41591194, -.48498794, 0, 0, .09911207, 0, -.46908805, .0205119, .0535119, -.14228792, -.55708808, 0, -.54008788, -.30998799, -.10958811, 0, -.01338812, 0, -.51788801, 0, 0, .13271193, -.11208793, 0, -.54508799, 0, .16641192, .95871216, 0, 1.6281118, 0, -.49718806, -.41348812, 0, 0, -.11718794, -.57058805, -.59488791, 0, -.65658802, 0, -.52698797, 0, 0, 0, 0, 0, 1.3500118, 0, 1.665812, 0, 1.963912, 0, 0, 1.9371119, .90991193, -.39558789, .39521196, 0, -.05268808, 0, 0, 0, 0, -.12458798, 0, -.28228804, .79281193, -.26358792, 0, 0, 0, -.72828788, 0, .355912, 0, 0, 0, 0, -.43538806, -1.566388, 0, -.28388807, 0, -.69028801, 0, -.78128809, -.54648799, -.92738789, 0, 0, .61571199, 0, 0, 1.012012, 0, 0, 0, 0, .43991187, .9404121, 0, 0, 0, .61671191, 0, 2.6073117, 0, -.60438794, 0, -.18108793, -.48178813, 0, -.22628804, -.07398792, 0, 0, 0, 1.830512, 0, 0, 0, 0, 0, 0, -.36918804, 1.3247118, 0, 0, 1.163012, 0, 0, 0, 1.3241119, 0, 0, 0, 0, -.90038794, -1.250888, 0, 0, 0, 0, -1.048188, -.90138787, 0, 0, 0, 0, 0, 0, -.00878807, 0, 0, .46301201, -.22048803, -.71518797, 0, 0, -.4952881, 0, -.83718795, .57951194, 0, 0, 0, 1.054112, .61721212, 2.2717118, 0, 2.0280118, 0, 0, 0, 2.6503119, 2.3914118, 0, -.36418793, -.9259879, 0, 0, .16971211, 0, 1.9360118, 2.5344119, 2.0171118, 0, 0, 0, 0, 0, 0, 0, 0, .77211195, 0, 0, 0, 0, 0, 1.952312, 0, 0, .25901201, 0, -.5028879, .03641204, 0, .38571194, 0, 0, -.44528791, -.55918807, 0, .39721206, 0, -.34038803, -.05988808, 0, -.03518792, .045512, 0, -.039288, 0, .01431207, 0, 0, .030412, -.31918809, 0, 0, 0, -.324388, 0, -1.232188, 0, -.23678799, -.89188808, 0, -.6766879, 0, 0, 0, 0, 0, 0, 0, -.67818803, 0, 1.099412, 1.2767119, -.64068788, -.50678796, 0, 0, 0, 0, 0, .68371207, .11251191, -.17128797, .17081194, 0, -.48708794, .09591202, 0, -.20108791, -.02158805, 0, -.3012881, 0, 0, 0, -.87638801, -.54488796, 0, 0, 0, 0, -.14738794, 0, 0, 0, 0, -.75718802, -.37418792, 0, 1.0981121, 1.1441121, .47381189, 0, 0, 0, -.33498809, 0, 0, 0, 0, -.91838807, 0, -.34488794, 0, .12971191, .99381214, -.91608804, 0, 0, .98171192, 0, 0, 0, 0, -.01528808, 0, -.41458794, .25691202, .18601207, 0, 0, 0, -.35608789, .79691201, 0, 0, 1.548912, 0, 0, 0, 0, 0, 0, 0, 0, 1.1663117, 0, 0, -.009088, -.49578807, 0, 0, -.2677879, 0, -.25468799, .68631202, 0, 0, 0, -.36198804])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data_cox(dta):\n",
    "    array = lambda x : np.array(dta[x], dtype=float)\n",
    "    t = array('t')\n",
    "    obs_t = array('obs_t')\n",
    "    pscenter = array('pscenter')\n",
    "    hhcenter = array('hhcenter')\n",
    "    ncomact = array('ncomact')\n",
    "    rleader = array('rleader')\n",
    "    dleader = array('dleader')\n",
    "    inter1 = array('inter1')\n",
    "    inter2 = array('inter2')\n",
    "    fail = array('FAIL')\n",
    "    return (t, obs_t, pscenter, hhcenter, ncomact,\n",
    "            rleader, dleader, inter1, inter2, fail)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "(t, obs_t, pscenter, hhcenter, ncomact, rleader,\n",
    "     dleader, inter1, inter2, fail) = load_data_cox(dta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array([pscenter, hhcenter, ncomact, rleader, dleader, inter1, inter2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7, 430)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO (theano.gof.compilelock): Waiting for existing lock by process '9951' (I am process '9684')\n",
      "INFO (theano.gof.compilelock): To manually release the lock, delete /Users/twiecki/.theano/compiledir_Darwin-18.2.0-x86_64-i386-64bit-i386-3.6.7-64/lock_dir\n",
      "INFO (theano.gof.compilelock): Waiting for existing lock by process '9951' (I am process '9684')\n",
      "INFO (theano.gof.compilelock): To manually release the lock, delete /Users/twiecki/.theano/compiledir_Darwin-18.2.0-x86_64-i386-64bit-i386-3.6.7-64/lock_dir\n"
     ]
    }
   ],
   "source": [
    "with Model() as model:\n",
    "    \n",
    "    T = len(t) - 1\n",
    "    nsubj = len(obs_t)\n",
    "\n",
    "    # risk set equals one if obs_t >= t\n",
    "    Y = np.array([[int(obs >= time) for time in t] for obs in obs_t])\n",
    "    # counting process. jump = 1 if obs_t \\in [t[j], t[j+1])\n",
    "    dN = np.array([[Y[i,j]*int(t[j+1] >= obs_t[i])*fail[i] for j in range(T)] for i in\n",
    "                    range(nsubj)])\n",
    "\n",
    "    c = Gamma('c', .0001, .00001)\n",
    "    r = Gamma('r', .001, .0001)\n",
    "    \n",
    "    dL0_star = r*np.diff(t)\n",
    "    \n",
    "    # prior mean hazard\n",
    "    mu = dL0_star * c \n",
    "    \n",
    "    dL0 = Gamma('dL0', mu, c, shape=T)\n",
    "\n",
    "    beta = Normal('beta', np.zeros(7),\n",
    "                  np.ones(7)*100, shape=7)\n",
    "\n",
    "    linear_model = tt.exp(tt.dot(X.T, beta))\n",
    "    idt = Y[:, :-1] * tt.outer(linear_model, dL0)\n",
    "\n",
    "    dn_like = Poisson('dn_like', idt, observed=dN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using advi_map...\n",
      "/Users/twiecki/working/projects/pymc/pymc3/tuning/starting.py:61: UserWarning: find_MAP should not be used to initialize the NUTS sampler, simply call pymc3.sample() and it will automatically initialize NUTS in a better way.\n",
      "  warnings.warn('find_MAP should not be used to initialize the NUTS sampler, simply call pymc3.sample() and it will automatically initialize NUTS in a better way.')\n",
      "logp = -2,511.5, ||grad|| = 0.19122: 100%|██████████| 127/127 [00:00<00:00, 563.62it/s] \n",
      "Average Loss = 2,837.4: 100%|██████████| 10000/10000 [00:14<00:00, 689.45it/s]\n",
      "Finished [100%]: Average Loss = 2,837.3\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [beta, dL0, r, c]\n",
      "Sampling 2 chains: 100%|██████████| 5000/5000 [00:55<00:00, 89.50draws/s] \n",
      "/Users/twiecki/anaconda3/lib/python3.6/site-packages/mkl_fft/_numpy_fft.py:1044: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  output = mkl_fft.rfftn_numpy(a, s, axes)\n"
     ]
    }
   ],
   "source": [
    "with model:\n",
    "    trace = sample(2000, n_init=10000, init='advi_map')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "traceplot(trace, var_names=['c', 'r']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x388.8 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "forestplot(trace, var_names=['beta']);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  },
  "latex_envs": {
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 0
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
